Spike variants
library(tidyverse)
setwd("~/Documents/research/asm/covid19-Assembly/plots/")
s = read.table("~/Documents/research/asm/covid19-Assembly/files/SpikeSNVsDetails.txt")
s2 = data.frame(str_split_fixed(s$V1, "_", 3), s)
#number of samples used for variant calling
length(unique(s2$X1))
## [1] 347
s3 = s2 %>% group_by(X2, V5) %>%
summarise(n = n())
ggplot(s3, aes(X2, n, fill = V5)) +
geom_bar(position="stack", stat="identity") +
xlab("") +
ylab("Total variants in 347 samples at Spike locus") +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 90, hjust = 1),
strip.background =element_rect(fill="white"),
legend.position = "right")
ggsave("Variant_count_spike.pdf", width = 18, height = 13, units = "cm", device = 'pdf')
## total count of variants
v = read.table("~/Documents/research/asm/covid19-Assembly/files/Variant_count_all_assembler.txt")
ggplot(v, aes(V1, V2, fill = V1)) +
geom_bar(position="stack", stat="identity") +
xlab("") +
ylab("Total variants in 416 samples") +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 90, hjust = 1),
strip.background =element_rect(fill="white"),
legend.position = "none")
ggsave("Variant_count_all.pdf", width = 18, height = 13, units = "cm", device = 'pdf')
# correlation
x = read_tsv("~/Documents/research/asm/covid19-Assembly/files/report_metaquast_PE_all.tsv")
x2 = melt(x, id.vars = "Assembly")
x2$Assembly = gsub("# ", "", x2$Assembly)
x3 = data.frame(str_split_fixed(x2$variable, "_", 3), value = as.numeric(x2$value), x2)
x4 = x3 %>% filter(Assembly == "Genome fraction (%)") %>% na.omit() %>%
group_by(X2) %>%
summarise(mean = mean(value))
cor(v$V2, x4$mean)
## [1] 0.734553
#0.734553
CPU and RAM
library(tidyverse)
setwd("~/Documents/research/asm/covid19-Assembly/plots/")
c = read_csv("~/Documents/research/asm/covid19-Assembly/files/CPU_time.csv")
ggplot(c, aes(Assembler, `Time(s)`)) +
geom_jitter(alpha = .5, width = .1, aes(color = Assembler)) +
geom_boxplot(alpha = 0.3) +
#scale_color_viridis(discrete=TRUE) +
#scale_fill_material("red") +
xlab("") +
#ylab(variableToPlot) +
#facet_grid(Assay_Type~.) +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 90, hjust = 1),
strip.background =element_rect(fill="white"),
legend.position = "none")
ggsave("CPU_times.pdf", width = 18, height = 13, units = "cm", device = 'pdf')
#ram
c = read_csv("~/Documents/research/asm/covid19-Assembly/files/RAM.csv")
ggplot(c, aes(Assembler, Ram_percentage)) +
geom_jitter(alpha = .5, width = .1, aes(color = Assembler)) +
geom_boxplot(alpha = 0.3) +
#scale_color_viridis(discrete=TRUE) +
#scale_fill_material("red") +
xlab("") +
#ylab(variableToPlot) +
#facet_grid(Assay_Type~.) +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 90, hjust = 1),
strip.background =element_rect(fill="white"),
legend.position = "none")
ggsave("RAM_usage.pdf", width = 18, height = 13, units = "cm", device = 'pdf')
variant heatmap
library(pheatmap)
library(tidyverse)
meta = read_csv("~/Documents/research/asm/covid19-Assembly/files/PE_561samples_final.csv")
mh = read.table("~/Documents/research/asm/covid19-Assembly/files/megahit_alignment_matrix_50bp.tsv", head = F)
ms = read.table("~/Documents/research/asm/covid19-Assembly/files/metaspade_alignment_matrix_50bp.tsv", head = F)
ab = read.table("~/Documents/research/asm/covid19-Assembly/files/abyss63_alignment_matrix_50bp.tsv", head = F)
tr = read.table("~/Documents/research/asm/covid19-Assembly/files/trinity_alignment_matrix_50bp.tsv", head = F)
hs = cbind(mh, ms, ab, tr)
hst = data.frame(t(hs))
hst2 = hst %>% filter(X1 != "A.ID.megahit.50bp_overlap") %>%
filter(X1 != "A.ID.metaspade.50bp_overlap") %>%
filter(X1 != "A.ID.abyss63.50bp_overlap") %>%
filter(X1 != "A.ID.trinity.50bp_overlap")
hst3 = data.frame(str_split_fixed(hst2$X1, "_", 3), hst2)
megahit = hst3 %>% filter(hst3$X2.1 == "megahit") %>% select(-X2.1) %>% select(-X3.1) %>% select(-X1)
metaspades = hst3 %>% filter(hst3$X2.1 == "metaspades") %>% select(-X2.1) %>% select(-X3.1) %>% select(-X1)
abyss = hst3 %>% filter(hst3$X2.1 == "abyss63") %>% select(-X2.1) %>% select(-X3.1) %>% select(-X1)
trinity = hst3 %>% filter(hst3$X2.1 == "trinity") %>% select(-X2.1) %>% select(-X3.1) %>% select(-X1)
both = merge(megahit, metaspades, by.x = "X1.1", by.y = "X1.1")
both = inner_join(megahit, metaspades, by = c("X1.1" = "X1.1")) %>%
inner_join(abyss, by = c("X1.1" = "X1.1")) %>%
inner_join(trinity, by = c("X1.1" = "X1.1"))
both2 = inner_join(meta, both, by = c( "Run" = "X1.1"))
both3 = both2[, c(2,6, 15:ncol(both2))]
both3 %>% group_by(Assay_Type) %>%
summarise(n = n())
## # A tibble: 5 x 2
## Assay_Type n
## <chr> <int>
## 1 AMPLICON 81
## 2 OTHER 60
## 3 RNA-Seq 41
## 4 Targeted-Capture 83
## 5 WGA 97
#write.table(both3, "~/Documents/research/asm/covid19-Assembly/files/megahit_metaspades_heatmap_matrix.tsv", sep = "\t", quote = F, row.names = F, col.names = F)
both4 = data.frame(both3[,3:ncol(both3)])
#conver factor to numeric
both5 = both4 %>%
mutate_all(~as.numeric(as.character(.)))
rownames(both5) = both3$Run
#pheatmap(both5, cluster_cols = F, cluster_rows = F, show_rownames = F, show_colnames = F, color = colorRampPalette(c("red", "white", "gray"))(1000), border_color = NA, gaps_col = 599)
#add annotation
anno = data.frame(Assay = both3$Assay_Type)
rownames(anno) = rownames(both5)
pheatmap(both5, annotation_row = anno, cluster_cols = F, cluster_rows = F, show_rownames = F, show_colnames = F, color = colorRampPalette(c("red", "white", "gray"))(1000), border_color = NA, gaps_col = c(599, 599+599, 599+599+599), gaps_row = c(82, 82+65, 82+65+58, 82+65+58+88))
plot.new()
jpeg("~/Documents/research/asm/covid19-Assembly/plots/heatmap_v2.jpg", width = 1800, height = 1000)
pheatmap(both5, annotation_row = anno, cluster_cols = F, cluster_rows = F, show_rownames = F, show_colnames = F, color = colorRampPalette(c("red", "white", "gray"))(1000), border_color = NA, gaps_col = c(599, 599+599, 599+599+599), gaps_row = c(81, 81+60, 81+60+41, 81+60+41+83))
dev.off()
## quartz_off_screen
## 3
#get best and worst asm
worst = data.frame(asm = rowSums(both5)) %>% arrange(asm) %>% head()
best = data.frame(asm = rowSums(both5)) %>% arrange(asm) %>% tail()
write.table(rbind(worst, best), "~/Documents/research/asm/covid19-Assembly/files/wrostAndBest_asm.tsv", col.names = F, quote = F)
#heatmap of worst and best
make_heatmap = function(SRR) {
SRR12182155 = hst3 %>% filter(hst3$X1.1 == SRR) %>% select(-X3.1) %>% select(-X1)
SRR12182155_v2 = SRR12182155 %>% select(-X1.1) %>% select(-X2.1)
#conver factor to numeric
SRR12182155_v3 = SRR12182155_v2 %>%
mutate_all(~as.numeric(as.character(.)))
rownames(SRR12182155_v3) = SRR12182155$X2.1
#
pheatmap(SRR12182155_v3, cluster_cols = F, cluster_rows = F, show_rownames = T, show_colnames = F, color = colorRampPalette(c("red", "white", "gray"))(1000), border_color = "black", gaps_row = c(1:3))
}
#worst 3
plot.new()
pdf("SRR11783612.pdf", width = 18, height = 2)
make_heatmap("SRR11783612") #Targeted-Capture
dev.off()
## quartz_off_screen
## 3
plot.new()
pdf("SRR11954291.pdf", width = 18, height = 2)
make_heatmap("SRR11954291") #RNA-seq; 4th
dev.off()
## quartz_off_screen
## 3
make_heatmap("SRR11783580") #Targeted-Capture
make_heatmap("ERR4321689") #WGA
#best 3
plot.new()
pdf("SRR12182155.pdf", width = 18, height = 2)
make_heatmap("SRR12182155") #OTHER
dev.off()
## quartz_off_screen
## 3
plot.new()
pdf("SRR11903415.pdf", width = 18, height = 2)
make_heatmap("SRR11903415") #Targeted-Capture; 6th and rest of them are Other
dev.off()
## quartz_off_screen
## 3
make_heatmap("SRR12182119") #OTHER
make_heatmap("SRR12182157") #OTHER
make_heatmap("SRR11903415") #Targeted-Capture; 6th and rest of them are Other
SRR12182155 = hst3 %>% filter(hst3$X1.1 == "SRR12182155") %>% select(-X3.1) %>% select(-X1)
SRR12182155_v2 = SRR12182155 %>% select(-X1.1) %>% select(-X2.1)
#conver factor to numeric
SRR12182155_v3 = SRR12182155_v2 %>%
mutate_all(~as.numeric(as.character(.)))
rownames(SRR12182155_v3) = SRR12182155$X2.1
pheatmap(SRR12182155_v3, cluster_cols = F, cluster_rows = F, show_rownames = T, show_colnames = F, color = colorRampPalette(c("red", "white", "gray"))(1000), border_color = "black", gaps_row = c(1:3))
#make genome. hand draw
g = read.table("~/Documents/research/asm/covid19-Assembly/files/GeneBank_Genes.bed")
g2 = data.frame(gene = g$V4, len = g$V3-g$V2, sp = g$V1)
ggplot(g2, aes(sp, len, fill = gene)) +
geom_bar(stat = "identity", aes(alpha = .0), color = "black") +
coord_flip()
assembly quality
library(tidyverse)
library(reshape2)
library(viridis)
library(ggsci)
#x = read_tsv("~/Documents/research/asm/covid19-Assembly/files/backup/bioRxiv_1074_assembly_report_PE_amplicon_viralRNA.tsv")
x = read_tsv("~/Documents/research/asm/covid19-Assembly/files/report_metaquast_PE_all.tsv")
x2 = melt(x, id.vars = "Assembly")
x2$Assembly = gsub("# ", "", x2$Assembly)
x3 = data.frame(str_split_fixed(x2$variable, "_", 3), value = as.numeric(x2$value), x2)
meta = read_csv("~/Documents/research/asm/covid19-Assembly/files/PE_561samples_final.csv")
meta2 = meta %>% select(Run, Assay_Type)
xm = left_join(x3, meta2, by = c("X1" = "Run"))
#number of samples in each category
xm %>% distinct(variable, .keep_all = T) %>%
filter(Assembly == "contigs (>= 0 bp)") %>%
group_by(Assay_Type, X2) %>%
summarise(n = n()) %>%
filter(n == max(n)) %>%
distinct(Assay_Type, .keep_all = T)
## # A tibble: 5 x 3
## # Groups: Assay_Type [5]
## Assay_Type X2 n
## <chr> <fct> <int>
## 1 AMPLICON megahit 82
## 2 OTHER abyss63 66
## 3 RNA-Seq abyss21 75
## 4 Targeted-Capture abyss63 93
## 5 WGA abyss21 100
#
setwd("~/Documents/research/asm/covid19-Assembly/plots/")
make_boxplot = function(variableToPlot)
{
x4 = xm %>% filter(Assembly == variableToPlot)
stat = x4 %>%
group_by(Assay_Type, X2) %>%
summarize(mean = mean(value, na.rm = TRUE),
median = median(value, na.rm = TRUE))
#kable(stat)
print(stat, n = Inf)
plot = ggplot(x4, aes(X2, value)) +
geom_jitter(alpha = .5, width = .1, aes(color = X2)) +
geom_boxplot(alpha = 0.1) +
#scale_color_viridis(discrete=TRUE) +
#scale_fill_material("red") +
xlab("") +
ylab(variableToPlot) +
facet_grid(Assay_Type~.) +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 90, hjust = 1),
strip.background =element_rect(fill="white"),
legend.position = "none")
print(plot)
filename = str_replace_all(variableToPlot, "[[:punct:]]", "")
ggsave(filename=paste(filename,".pdf", sep="_"), width = 12, height = 18, units = "cm", device = 'pdf')
}
make_boxplot("Genome fraction (%)")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 84.8 94.8
## 2 AMPLICON abyss63 83.5 95.6
## 3 AMPLICON abyss99 70.6 81.5
## 4 AMPLICON megahit 97.5 99.7
## 5 AMPLICON metaspades 97.5 99.7
## 6 AMPLICON metavelvet21 7.03 3.89
## 7 AMPLICON metavelvet63 23.5 10.6
## 8 AMPLICON metavelvet99 25.2 21.3
## 9 AMPLICON ray21 90.4 94.8
## 10 AMPLICON ray63 91.6 95.0
## 11 AMPLICON ray99 91.6 95.0
## 12 AMPLICON spades 96.7 99.2
## 13 AMPLICON trinity 81.4 98.0
## 14 AMPLICON velvet21 7.15 4.78
## 15 AMPLICON velvet63 23.8 11.2
## 16 AMPLICON velvet99 25.7 21.1
## 17 OTHER abyss21 81.0 94.1
## 18 OTHER abyss63 84.3 97.9
## 19 OTHER abyss99 83.7 99.5
## 20 OTHER megahit 92.3 99.8
## 21 OTHER metaspades 92.3 99.8
## 22 OTHER metavelvet21 39.8 33.8
## 23 OTHER metavelvet63 36.3 24.5
## 24 OTHER metavelvet99 47.5 55.4
## 25 OTHER ray21 81.4 97.8
## 26 OTHER ray63 87.6 99.2
## 27 OTHER ray99 87.6 99.2
## 28 OTHER spades 89.9 99.9
## 29 OTHER trinity 91.6 99.9
## 30 OTHER velvet21 39.6 33.8
## 31 OTHER velvet63 37.5 26.0
## 32 OTHER velvet99 47.8 57.5
## 33 RNA-Seq abyss21 23.2 8.85
## 34 RNA-Seq abyss63 56.2 64.3
## 35 RNA-Seq abyss99 69.8 90.1
## 36 RNA-Seq megahit 81.8 99.8
## 37 RNA-Seq metaspades 82.6 99.8
## 38 RNA-Seq metavelvet21 12.0 4.25
## 39 RNA-Seq metavelvet63 48.6 55.6
## 40 RNA-Seq metavelvet99 59.3 65.2
## 41 RNA-Seq ray21 88.7 99.8
## 42 RNA-Seq ray63 86.6 99.8
## 43 RNA-Seq ray99 86.6 99.8
## 44 RNA-Seq spades 85.4 99.8
## 45 RNA-Seq trinity 81.4 95.9
## 46 RNA-Seq velvet21 12.4 3.99
## 47 RNA-Seq velvet63 48.6 55.6
## 48 RNA-Seq velvet99 59.4 65.2
## 49 Targeted-Capture abyss21 85.1 95.2
## 50 Targeted-Capture abyss63 74.1 98.0
## 51 Targeted-Capture abyss99 71.6 97.4
## 52 Targeted-Capture megahit 92.3 99.8
## 53 Targeted-Capture metaspades 89.4 99.8
## 54 Targeted-Capture metavelvet21 22.2 14.1
## 55 Targeted-Capture metavelvet63 32.0 25.3
## 56 Targeted-Capture metavelvet99 58.4 73.1
## 57 Targeted-Capture ray21 84.3 97.8
## 58 Targeted-Capture ray63 84.4 96.9
## 59 Targeted-Capture ray99 84.4 96.9
## 60 Targeted-Capture spades 93.1 99.8
## 61 Targeted-Capture trinity 76.3 88.0
## 62 Targeted-Capture velvet21 22.0 14.1
## 63 Targeted-Capture velvet63 32.1 27.6
## 64 Targeted-Capture velvet99 60.0 74.9
## 65 WGA abyss21 87.4 98.2
## 66 WGA abyss63 88.8 98.8
## 67 WGA abyss99 71.9 82.9
## 68 WGA megahit 93.6 99.7
## 69 WGA metaspades 94.4 99.7
## 70 WGA metavelvet21 7.12 2.19
## 71 WGA metavelvet63 14.7 9.61
## 72 WGA metavelvet99 21.6 21.2
## 73 WGA ray21 89.4 99.4
## 74 WGA ray63 90.0 99.2
## 75 WGA ray99 90.0 99.2
## 76 WGA spades 93.8 99.7
## 77 WGA trinity 95.3 99.7
## 78 WGA velvet21 7.34 2.93
## 79 WGA velvet63 14.8 9.99
## 80 WGA velvet99 21.3 20.5
make_boxplot("Largest contig")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 12912. 12340.
## 2 AMPLICON abyss63 10376. 6950
## 3 AMPLICON abyss99 5917. 2826
## 4 AMPLICON megahit 23680. 28204
## 5 AMPLICON metaspades 23846. 29828
## 6 AMPLICON metavelvet21 755. 667
## 7 AMPLICON metavelvet63 1521. 814
## 8 AMPLICON metavelvet99 1120. 991
## 9 AMPLICON ray21 15248. 14898.
## 10 AMPLICON ray63 14237. 13042
## 11 AMPLICON ray99 14216. 13042
## 12 AMPLICON spades 18965. 17698.
## 13 AMPLICON trinity 16179. 17251
## 14 AMPLICON velvet21 755. 667
## 15 AMPLICON velvet63 1512. 810
## 16 AMPLICON velvet99 1140. 1008
## 17 OTHER abyss21 13277. 9763
## 18 OTHER abyss63 16164. 16418
## 19 OTHER abyss99 18346. 20620.
## 20 OTHER megahit 22914. 29860.
## 21 OTHER metaspades 20734. 25414
## 22 OTHER metavelvet21 1373. 977
## 23 OTHER metavelvet63 1708. 1103
## 24 OTHER metavelvet99 2325. 1764
## 25 OTHER ray21 17657. 24671
## 26 OTHER ray63 19362. 27522
## 27 OTHER ray99 19338. 27522
## 28 OTHER spades 21998. 29858.
## 29 OTHER trinity 20004. 27898
## 30 OTHER velvet21 1335. 968
## 31 OTHER velvet63 1762. 1149
## 32 OTHER velvet99 2382. 1893
## 33 RNA-Seq abyss21 2264. 1630
## 34 RNA-Seq abyss63 3564. 1746.
## 35 RNA-Seq abyss99 4425. 884
## 36 RNA-Seq megahit 19432. 28035
## 37 RNA-Seq metaspades 17937. 22139
## 38 RNA-Seq metavelvet21 1009. 936
## 39 RNA-Seq metavelvet63 2007. 1447
## 40 RNA-Seq metavelvet99 1914. 1535
## 41 RNA-Seq ray21 19262. 15401
## 42 RNA-Seq ray63 16517. 15085
## 43 RNA-Seq ray99 16517. 15085
## 44 RNA-Seq spades 24970. 28525
## 45 RNA-Seq trinity 11322. 6911
## 46 RNA-Seq velvet21 1024. 968.
## 47 RNA-Seq velvet63 1993. 1399
## 48 RNA-Seq velvet99 1935. 1535
## 49 Targeted-Capture abyss21 13851. 12703
## 50 Targeted-Capture abyss63 14562. 7349
## 51 Targeted-Capture abyss99 13963. 6738.
## 52 Targeted-Capture megahit 20637. 29853
## 53 Targeted-Capture metaspades 19423. 25414
## 54 Targeted-Capture metavelvet21 965. 777
## 55 Targeted-Capture metavelvet63 1470. 988.
## 56 Targeted-Capture metavelvet99 3822. 2390
## 57 Targeted-Capture ray21 17318. 20228
## 58 Targeted-Capture ray63 16779. 19716
## 59 Targeted-Capture ray99 16779. 19716
## 60 Targeted-Capture spades 22070. 29850
## 61 Targeted-Capture trinity 12757. 9994
## 62 Targeted-Capture velvet21 974. 792.
## 63 Targeted-Capture velvet63 1449. 997
## 64 Targeted-Capture velvet99 3783. 2367
## 65 WGA abyss21 16229. 22264.
## 66 WGA abyss63 11023 9808.
## 67 WGA abyss99 4135. 2970.
## 68 WGA megahit 25966. 29842.
## 69 WGA metaspades 25836. 29844
## 70 WGA metavelvet21 895. 726.
## 71 WGA metavelvet63 1011. 873
## 72 WGA metavelvet99 1132. 1080.
## 73 WGA ray21 24301. 28606.
## 74 WGA ray63 23888. 28856.
## 75 WGA ray99 23888. 28856.
## 76 WGA spades 15932. 15251
## 77 WGA trinity 24308. 29819
## 78 WGA velvet21 889. 726.
## 79 WGA velvet63 1027. 874
## 80 WGA velvet99 1154. 1072.
make_boxplot("Total length")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 25718. 28690.
## 2 AMPLICON abyss63 25518. 28960
## 3 AMPLICON abyss99 21873. 25571
## 4 AMPLICON megahit 32533. 29884.
## 5 AMPLICON metaspades 31636. 29892.
## 6 AMPLICON metavelvet21 2804. 1430
## 7 AMPLICON metavelvet63 7677. 3507
## 8 AMPLICON metavelvet99 8651. 6620
## 9 AMPLICON ray21 30382. 30377
## 10 AMPLICON ray63 30308. 29811
## 11 AMPLICON ray99 30306. 29811
## 12 AMPLICON spades 39610. 30484.
## 13 AMPLICON trinity 32700. 29827
## 14 AMPLICON velvet21 2803. 1430
## 15 AMPLICON velvet63 7741. 3523
## 16 AMPLICON velvet99 8799. 6710
## 17 OTHER abyss21 41145. 28522
## 18 OTHER abyss63 41983. 29727
## 19 OTHER abyss99 38973. 29865
## 20 OTHER megahit 82116. 37682.
## 21 OTHER metaspades 73609. 37646
## 22 OTHER metavelvet21 30511. 5816
## 23 OTHER metavelvet63 27464. 7386
## 24 OTHER metavelvet99 30918. 20079
## 25 OTHER ray21 43684. 32588.
## 26 OTHER ray63 44620. 32438
## 27 OTHER ray99 44612. 32438
## 28 OTHER spades 72647. 36342
## 29 OTHER trinity 106164. 53000.
## 30 OTHER velvet21 30349. 5564
## 31 OTHER velvet63 28805. 8920.
## 32 OTHER velvet99 31052. 20382
## 33 RNA-Seq abyss21 381196. 47320
## 34 RNA-Seq abyss63 150567. 24854
## 35 RNA-Seq abyss99 13802. 3444
## 36 RNA-Seq megahit 710616. 162712
## 37 RNA-Seq metaspades 797222. 191014
## 38 RNA-Seq metavelvet21 108844. 18099
## 39 RNA-Seq metavelvet63 139454. 22344
## 40 RNA-Seq metavelvet99 10470. 3655
## 41 RNA-Seq ray21 340292. 78097
## 42 RNA-Seq ray63 261610. 58810
## 43 RNA-Seq ray99 261634. 58810
## 44 RNA-Seq spades 1322823. 517283
## 45 RNA-Seq trinity 822573. 202200
## 46 RNA-Seq velvet21 110241. 24172.
## 47 RNA-Seq velvet63 139480. 22481
## 48 RNA-Seq velvet99 10527. 3655
## 49 Targeted-Capture abyss21 24382. 28166.
## 50 Targeted-Capture abyss63 24165. 27627
## 51 Targeted-Capture abyss99 24829. 27872.
## 52 Targeted-Capture megahit 106973. 29882
## 53 Targeted-Capture metaspades 109558. 29971
## 54 Targeted-Capture metavelvet21 48069. 3492.
## 55 Targeted-Capture metavelvet63 31935. 7293
## 56 Targeted-Capture metavelvet99 31915. 22440
## 57 Targeted-Capture ray21 51052. 29870
## 58 Targeted-Capture ray63 46045. 29859
## 59 Targeted-Capture ray99 46017. 29859
## 60 Targeted-Capture spades 295058. 36229
## 61 Targeted-Capture trinity 91350. 34724
## 62 Targeted-Capture velvet21 48257. 3472.
## 63 Targeted-Capture velvet63 32006. 7336.
## 64 Targeted-Capture velvet99 31934. 22694
## 65 WGA abyss21 31399. 29622.
## 66 WGA abyss63 36442. 29858.
## 67 WGA abyss99 30938. 27638
## 68 WGA megahit 62158. 36598
## 69 WGA metaspades 55143. 35172
## 70 WGA metavelvet21 11704. 2616.
## 71 WGA metavelvet63 16290. 6121
## 72 WGA metavelvet99 17129. 9537
## 73 WGA ray21 44307. 34880.
## 74 WGA ray63 45074. 33598.
## 75 WGA ray99 45081. 33598.
## 76 WGA spades 55083. 33648.
## 77 WGA trinity 65325. 39306.
## 78 WGA velvet21 11787. 2892.
## 79 WGA velvet63 16298. 6313
## 80 WGA velvet99 17133. 9277
make_boxplot("contigs")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 7.76 5
## 2 AMPLICON abyss63 10.2 9
## 3 AMPLICON abyss99 13.9 14.5
## 4 AMPLICON megahit 6.52 2
## 5 AMPLICON metaspades 5.37 2
## 6 AMPLICON metavelvet21 4.32 2
## 7 AMPLICON metavelvet63 8 5
## 8 AMPLICON metavelvet99 11.4 10
## 9 AMPLICON ray21 7.56 4.5
## 10 AMPLICON ray63 8.11 5
## 11 AMPLICON ray99 8.12 5
## 12 AMPLICON spades 8.48 5
## 13 AMPLICON trinity 8.79 3.5
## 14 AMPLICON velvet21 4.32 2
## 15 AMPLICON velvet63 8.14 5
## 16 AMPLICON velvet99 11.6 10
## 17 OTHER abyss21 32.0 8
## 18 OTHER abyss63 28.8 5.5
## 19 OTHER abyss99 22.7 3.5
## 20 OTHER megahit 65.8 11.5
## 21 OTHER metaspades 57.2 12
## 22 OTHER metavelvet21 42.1 9
## 23 OTHER metavelvet63 36.1 11
## 24 OTHER metavelvet99 36.3 18
## 25 OTHER ray21 24.3 9
## 26 OTHER ray63 23.5 8
## 27 OTHER ray99 23.5 8
## 28 OTHER spades 47.5 10
## 29 OTHER trinity 77.6 19.5
## 30 OTHER velvet21 42.0 8
## 31 OTHER velvet63 37.8 13.5
## 32 OTHER velvet99 36.3 18
## 33 RNA-Seq abyss21 438. 68
## 34 RNA-Seq abyss63 183. 26
## 35 RNA-Seq abyss99 10.1 3
## 36 RNA-Seq megahit 629. 160
## 37 RNA-Seq metaspades 754. 189
## 38 RNA-Seq metavelvet21 175. 31
## 39 RNA-Seq metavelvet63 179. 27
## 40 RNA-Seq metavelvet99 10.4 3
## 41 RNA-Seq ray21 314. 52
## 42 RNA-Seq ray63 253. 44
## 43 RNA-Seq ray99 254. 44
## 44 RNA-Seq spades 787. 277
## 45 RNA-Seq trinity 863. 206
## 46 RNA-Seq velvet21 178. 41.5
## 47 RNA-Seq velvet63 179. 28
## 48 RNA-Seq velvet99 10.4 3
## 49 Targeted-Capture abyss21 10.9 5
## 50 Targeted-Capture abyss63 10.8 4
## 51 Targeted-Capture abyss99 11.9 4
## 52 Targeted-Capture megahit 109. 4
## 53 Targeted-Capture metaspades 106. 5
## 54 Targeted-Capture metavelvet21 73.5 6
## 55 Targeted-Capture metavelvet63 45.9 10.5
## 56 Targeted-Capture metavelvet99 34 11
## 57 Targeted-Capture ray21 38.9 7
## 58 Targeted-Capture ray63 34.6 10
## 59 Targeted-Capture ray99 34.6 10
## 60 Targeted-Capture spades 182. 3
## 61 Targeted-Capture trinity 82.7 9
## 62 Targeted-Capture velvet21 73.9 6
## 63 Targeted-Capture velvet63 46.0 10
## 64 Targeted-Capture velvet99 34.1 11
## 65 WGA abyss21 13.7 4
## 66 WGA abyss63 21.9 7
## 67 WGA abyss99 29.0 21
## 68 WGA megahit 43.9 10.5
## 69 WGA metaspades 35.6 8.5
## 70 WGA metavelvet21 17.7 4.5
## 71 WGA metavelvet63 24.1 10
## 72 WGA metavelvet99 24.9 14.5
## 73 WGA ray21 17.9 5
## 74 WGA ray63 19.9 4
## 75 WGA ray99 19.9 4
## 76 WGA spades 34.9 8
## 77 WGA trinity 45.6 12
## 78 WGA velvet21 17.9 5
## 79 WGA velvet63 24.0 10
## 80 WGA velvet99 24.9 13.5
make_boxplot("contigs (>= 1000 bp)")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 3.88 3
## 2 AMPLICON abyss63 5.38 5
## 3 AMPLICON abyss99 5.36 5.5
## 4 AMPLICON megahit 3.09 2
## 5 AMPLICON metaspades 2.79 1
## 6 AMPLICON metavelvet21 0.32 0
## 7 AMPLICON metavelvet63 1.76 0
## 8 AMPLICON metavelvet99 1.56 0
## 9 AMPLICON ray21 5.45 4
## 10 AMPLICON ray63 5.89 4
## 11 AMPLICON ray99 5.90 4
## 12 AMPLICON spades 5.67 4
## 13 AMPLICON trinity 4.54 3
## 14 AMPLICON velvet21 0.32 0
## 15 AMPLICON velvet63 1.71 0
## 16 AMPLICON velvet99 1.56 1
## 17 OTHER abyss21 7.38 3
## 18 OTHER abyss63 7.35 2
## 19 OTHER abyss99 5.36 2
## 20 OTHER megahit 15.3 5
## 21 OTHER metaspades 14.5 5.5
## 22 OTHER metavelvet21 4.4 0
## 23 OTHER metavelvet63 4.83 1
## 24 OTHER metavelvet99 7.16 5
## 25 OTHER ray21 9.03 5
## 26 OTHER ray63 8.57 5
## 27 OTHER ray99 8.57 5
## 28 OTHER spades 14.0 4
## 29 OTHER trinity 22.8 10
## 30 OTHER velvet21 4.29 0
## 31 OTHER velvet63 5.08 1
## 32 OTHER velvet99 7.06 5
## 33 RNA-Seq abyss21 92.1 4
## 34 RNA-Seq abyss63 31.5 5
## 35 RNA-Seq abyss99 2.44 0
## 36 RNA-Seq megahit 174. 31
## 37 RNA-Seq metaspades 194. 26
## 38 RNA-Seq metavelvet21 3.90 0
## 39 RNA-Seq metavelvet63 28.4 5
## 40 RNA-Seq metavelvet99 3.12 2
## 41 RNA-Seq ray21 95.6 15
## 42 RNA-Seq ray63 70.9 15
## 43 RNA-Seq ray99 70.9 15
## 44 RNA-Seq spades 272. 123
## 45 RNA-Seq trinity 202. 37
## 46 RNA-Seq velvet21 3.86 0
## 47 RNA-Seq velvet63 28.4 5
## 48 RNA-Seq velvet99 3.15 2
## 49 Targeted-Capture abyss21 3.46 2
## 50 Targeted-Capture abyss63 3.19 1
## 51 Targeted-Capture abyss99 3.63 1
## 52 Targeted-Capture megahit 16.0 3
## 53 Targeted-Capture metaspades 19.2 3
## 54 Targeted-Capture metavelvet21 4.56 0
## 55 Targeted-Capture metavelvet63 3.87 0
## 56 Targeted-Capture metavelvet99 6.64 5
## 57 Targeted-Capture ray21 7.57 2
## 58 Targeted-Capture ray63 6.95 2
## 59 Targeted-Capture ray99 6.91 2
## 60 Targeted-Capture spades 33.0 3
## 61 Targeted-Capture trinity 18.1 5
## 62 Targeted-Capture velvet21 4.58 0
## 63 Targeted-Capture velvet63 3.89 0.5
## 64 Targeted-Capture velvet99 6.88 5
## 65 WGA abyss21 3.82 3
## 66 WGA abyss63 7.42 6
## 67 WGA abyss99 8.76 9
## 68 WGA megahit 9.55 2
## 69 WGA metaspades 8.52 2
## 70 WGA metavelvet21 1.10 0
## 71 WGA metavelvet63 1.76 0
## 72 WGA metavelvet99 2.25 1
## 73 WGA ray21 6.27 2
## 74 WGA ray63 6.8 2
## 75 WGA ray99 6.8 2
## 76 WGA spades 11.0 5
## 77 WGA trinity 9.61 3
## 78 WGA velvet21 1.15 0
## 79 WGA velvet63 1.92 0
## 80 WGA velvet99 2.32 1
make_boxplot("Largest alignment")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 13111. 12314.
## 2 AMPLICON abyss63 10592. 6996
## 3 AMPLICON abyss99 6017. 2917
## 4 AMPLICON megahit 23870. 29079
## 5 AMPLICON metaspades 24064. 29815
## 6 AMPLICON metavelvet21 779. 681
## 7 AMPLICON metavelvet63 1593. 841
## 8 AMPLICON metavelvet99 1080. 969
## 9 AMPLICON ray21 14817. 13799
## 10 AMPLICON ray63 14233. 13346
## 11 AMPLICON ray99 14212. 13346
## 12 AMPLICON spades 18527. 18945
## 13 AMPLICON trinity 15790 16706
## 14 AMPLICON velvet21 782. 681
## 15 AMPLICON velvet63 1585. 822.
## 16 AMPLICON velvet99 1098 967
## 17 OTHER abyss21 14366. 12821
## 18 OTHER abyss63 17993. 20560
## 19 OTHER abyss99 19405. 27571
## 20 OTHER megahit 23181. 29834.
## 21 OTHER metaspades 20801. 25414
## 22 OTHER metavelvet21 1785. 1410
## 23 OTHER metavelvet63 1845. 1155
## 24 OTHER metavelvet99 2234. 1804
## 25 OTHER ray21 17366. 22939
## 26 OTHER ray63 19797. 27601
## 27 OTHER ray99 19771. 27601
## 28 OTHER spades 22337. 29856.
## 29 OTHER trinity 20306. 29850
## 30 OTHER velvet21 1806. 1410
## 31 OTHER velvet63 1843. 1072.
## 32 OTHER velvet99 2278. 1869
## 33 RNA-Seq abyss21 1583. 824.
## 34 RNA-Seq abyss63 5355. 2833
## 35 RNA-Seq abyss99 8550. 4142
## 36 RNA-Seq megahit 21627. 29831
## 37 RNA-Seq metaspades 20421. 29798
## 38 RNA-Seq metavelvet21 755. 724.
## 39 RNA-Seq metavelvet63 2407. 1858
## 40 RNA-Seq metavelvet99 3105. 1699
## 41 RNA-Seq ray21 21508. 25418
## 42 RNA-Seq ray63 20577. 25122
## 43 RNA-Seq ray99 20577. 25122
## 44 RNA-Seq spades 22124. 29839
## 45 RNA-Seq trinity 9685. 7338.
## 46 RNA-Seq velvet21 760 727
## 47 RNA-Seq velvet63 2407. 1858
## 48 RNA-Seq velvet99 3100. 1699
## 49 Targeted-Capture abyss21 18033. 24962.
## 50 Targeted-Capture abyss63 16996. 25428
## 51 Targeted-Capture abyss99 15980. 12836
## 52 Targeted-Capture megahit 21403. 29850.
## 53 Targeted-Capture metaspades 19491. 25414
## 54 Targeted-Capture metavelvet21 1027. 882.
## 55 Targeted-Capture metavelvet63 1609 1174
## 56 Targeted-Capture metavelvet99 4081. 2914
## 57 Targeted-Capture ray21 18079. 22934
## 58 Targeted-Capture ray63 17771. 26737
## 59 Targeted-Capture ray99 17771. 26737
## 60 Targeted-Capture spades 21153. 29790
## 61 Targeted-Capture trinity 13278. 10347
## 62 Targeted-Capture velvet21 1033. 883
## 63 Targeted-Capture velvet63 1583. 1174
## 64 Targeted-Capture velvet99 4133. 2914
## 65 WGA abyss21 17413. 22277
## 66 WGA abyss63 11329. 9994
## 67 WGA abyss99 4083. 3084
## 68 WGA megahit 26165 29819
## 69 WGA metaspades 26407. 29818
## 70 WGA metavelvet21 701. 624
## 71 WGA metavelvet63 856. 765
## 72 WGA metavelvet99 933. 846.
## 73 WGA ray21 22195. 26497
## 74 WGA ray63 22741. 27092
## 75 WGA ray99 22741. 27092
## 76 WGA spades 16099. 15298
## 77 WGA trinity 25182. 29819
## 78 WGA velvet21 698. 638.
## 79 WGA velvet63 866. 758.
## 80 WGA velvet99 934. 863
make_boxplot("mismatches per 100 kbp")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 24.6 25.3
## 2 AMPLICON abyss63 27.1 26.9
## 3 AMPLICON abyss99 27.4 25.6
## 4 AMPLICON megahit 28.8 27.2
## 5 AMPLICON metaspades 26.6 26.8
## 6 AMPLICON metavelvet21 61.1 0
## 7 AMPLICON metavelvet63 40.4 19.6
## 8 AMPLICON metavelvet99 58.1 31.1
## 9 AMPLICON ray21 38.9 36.9
## 10 AMPLICON ray63 39.4 37.7
## 11 AMPLICON ray99 39.3 37.7
## 12 AMPLICON spades 55.2 36.9
## 13 AMPLICON trinity 35.8 30.8
## 14 AMPLICON velvet21 61.1 0
## 15 AMPLICON velvet63 40.2 19.4
## 16 AMPLICON velvet99 61.8 34.8
## 17 OTHER abyss21 31.0 28.1
## 18 OTHER abyss63 29.8 27.4
## 19 OTHER abyss99 29.2 26.9
## 20 OTHER megahit 38.1 33.5
## 21 OTHER metaspades 36.5 33.5
## 22 OTHER metavelvet21 64.5 32.1
## 23 OTHER metavelvet63 40.0 27.2
## 24 OTHER metavelvet99 41.2 25.4
## 25 OTHER ray21 34.8 30.8
## 26 OTHER ray63 33.6 30.7
## 27 OTHER ray99 33.5 30.7
## 28 OTHER spades 61.0 36.8
## 29 OTHER trinity 55.7 43.5
## 30 OTHER velvet21 63.6 45.9
## 31 OTHER velvet63 42.0 27.5
## 32 OTHER velvet99 42.6 25.6
## 33 RNA-Seq abyss21 33.0 0
## 34 RNA-Seq abyss63 19.3 19.4
## 35 RNA-Seq abyss99 25.7 26.2
## 36 RNA-Seq megahit 32.5 26.8
## 37 RNA-Seq metaspades 36.9 26.8
## 38 RNA-Seq metavelvet21 60.7 14.4
## 39 RNA-Seq metavelvet63 18.9 18.1
## 40 RNA-Seq metavelvet99 36.1 25.2
## 41 RNA-Seq ray21 42.8 40.2
## 42 RNA-Seq ray63 33.3 32.5
## 43 RNA-Seq ray99 33.3 32.5
## 44 RNA-Seq spades 798. 43.6
## 45 RNA-Seq trinity 61.2 38.9
## 46 RNA-Seq velvet21 51.2 8.66
## 47 RNA-Seq velvet63 18.9 18.1
## 48 RNA-Seq velvet99 36.0 25.2
## 49 Targeted-Capture abyss21 24.6 24.7
## 50 Targeted-Capture abyss63 27.4 25.0
## 51 Targeted-Capture abyss99 28.4 26.0
## 52 Targeted-Capture megahit 37.9 26.8
## 53 Targeted-Capture metaspades 38.6 27.1
## 54 Targeted-Capture metavelvet21 27.8 21.1
## 55 Targeted-Capture metavelvet63 31.1 25.5
## 56 Targeted-Capture metavelvet99 26.5 19.2
## 57 Targeted-Capture ray21 32.1 30.1
## 58 Targeted-Capture ray63 35.8 30.1
## 59 Targeted-Capture ray99 35.8 30.1
## 60 Targeted-Capture spades 245. 40.2
## 61 Targeted-Capture trinity 64.6 36.8
## 62 Targeted-Capture velvet21 28.1 21.1
## 63 Targeted-Capture velvet63 31.6 23.9
## 64 Targeted-Capture velvet99 27.8 20.7
## 65 WGA abyss21 26.8 23.8
## 66 WGA abyss63 26.4 23.9
## 67 WGA abyss99 23.8 24.2
## 68 WGA megahit 32.3 26.8
## 69 WGA metaspades 28.9 26.8
## 70 WGA metavelvet21 26.4 0
## 71 WGA metavelvet63 31.6 21.5
## 72 WGA metavelvet99 49.9 45.1
## 73 WGA ray21 43.1 33.5
## 74 WGA ray63 36.5 33.5
## 75 WGA ray99 36.5 33.5
## 76 WGA spades 29.7 26.8
## 77 WGA trinity 33.3 28.0
## 78 WGA velvet21 28.8 0
## 79 WGA velvet63 31.4 22.0
## 80 WGA velvet99 43.0 35.4
make_boxplot("indels per 100 kbp")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 9.77 3.48
## 2 AMPLICON abyss63 5.69 3.39
## 3 AMPLICON abyss99 7.38 0
## 4 AMPLICON megahit 1.80 0
## 5 AMPLICON metaspades 0.966 0
## 6 AMPLICON metavelvet21 4.52 0
## 7 AMPLICON metavelvet63 4.21 0
## 8 AMPLICON metavelvet99 23.0 0
## 9 AMPLICON ray21 7.23 3.35
## 10 AMPLICON ray63 9.44 3.61
## 11 AMPLICON ray99 9.41 3.61
## 12 AMPLICON spades 12.1 3.42
## 13 AMPLICON trinity 4.80 0
## 14 AMPLICON velvet21 2.87 0
## 15 AMPLICON velvet63 3.48 0
## 16 AMPLICON velvet99 25.6 0
## 17 OTHER abyss21 2.49 0
## 18 OTHER abyss63 2.29 0
## 19 OTHER abyss99 1.15 0
## 20 OTHER megahit 1.54 0
## 21 OTHER metaspades 0.939 0
## 22 OTHER metavelvet21 4.61 0
## 23 OTHER metavelvet63 1.26 0
## 24 OTHER metavelvet99 1.27 0
## 25 OTHER ray21 0.625 0
## 26 OTHER ray63 0.656 0
## 27 OTHER ray99 0.656 0
## 28 OTHER spades 4.38 0
## 29 OTHER trinity 3.61 0
## 30 OTHER velvet21 4.69 0
## 31 OTHER velvet63 1.05 0
## 32 OTHER velvet99 1.05 0
## 33 RNA-Seq abyss21 0.499 0
## 34 RNA-Seq abyss63 3.75 0
## 35 RNA-Seq abyss99 1.80 0
## 36 RNA-Seq megahit 0.120 0
## 37 RNA-Seq metaspades 8.70 0
## 38 RNA-Seq metavelvet21 0 0
## 39 RNA-Seq metavelvet63 0 0
## 40 RNA-Seq metavelvet99 0 0
## 41 RNA-Seq ray21 0.316 0
## 42 RNA-Seq ray63 0.237 0
## 43 RNA-Seq ray99 0.237 0
## 44 RNA-Seq spades 7.41 0
## 45 RNA-Seq trinity 1.01 0
## 46 RNA-Seq velvet21 0 0
## 47 RNA-Seq velvet63 0 0
## 48 RNA-Seq velvet99 0 0
## 49 Targeted-Capture abyss21 7.20 3.5
## 50 Targeted-Capture abyss63 8.36 3.35
## 51 Targeted-Capture abyss99 3.10 3.35
## 52 Targeted-Capture megahit 6.09 0
## 53 Targeted-Capture metaspades 12.8 0
## 54 Targeted-Capture metavelvet21 6.67 0
## 55 Targeted-Capture metavelvet63 6.41 0
## 56 Targeted-Capture metavelvet99 1.37 0
## 57 Targeted-Capture ray21 2.54 0
## 58 Targeted-Capture ray63 3.41 0
## 59 Targeted-Capture ray99 3.41 0
## 60 Targeted-Capture spades 56.4 6.7
## 61 Targeted-Capture trinity 15.8 3.35
## 62 Targeted-Capture velvet21 17.9 0
## 63 Targeted-Capture velvet63 5.70 0
## 64 Targeted-Capture velvet99 1.37 0
## 65 WGA abyss21 7.14 0
## 66 WGA abyss63 5.13 0
## 67 WGA abyss99 4.16 0
## 68 WGA megahit 7.78 0
## 69 WGA metaspades 2.28 0
## 70 WGA metavelvet21 6.16 0
## 71 WGA metavelvet63 7.47 0
## 72 WGA metavelvet99 22.0 17.2
## 73 WGA ray21 15.0 6.71
## 74 WGA ray63 8.91 3.44
## 75 WGA ray99 8.91 3.44
## 76 WGA spades 4.01 0
## 77 WGA trinity 6.66 0
## 78 WGA velvet21 6.81 0
## 79 WGA velvet63 8.93 0
## 80 WGA velvet99 19.9 16.8
make_boxplot("Total aligned length")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 25541. 28664.
## 2 AMPLICON abyss63 25150. 28892
## 3 AMPLICON abyss99 21509. 25098.
## 4 AMPLICON megahit 29217. 29814
## 5 AMPLICON metaspades 29203. 29821
## 6 AMPLICON metavelvet21 2103. 1165
## 7 AMPLICON metavelvet63 7078. 3156.
## 8 AMPLICON metavelvet99 7585. 6362.
## 9 AMPLICON ray21 29294. 29823
## 10 AMPLICON ray63 29062. 29740
## 11 AMPLICON ray99 29060. 29740
## 12 AMPLICON spades 36456. 29930
## 13 AMPLICON trinity 30214. 29819
## 14 AMPLICON velvet21 2139. 1430
## 15 AMPLICON velvet63 7143. 3334.
## 16 AMPLICON velvet99 7733. 6378
## 17 OTHER abyss21 24277. 28223
## 18 OTHER abyss63 25334. 29417
## 19 OTHER abyss99 25135. 29846
## 20 OTHER megahit 27789. 29869
## 21 OTHER metaspades 27803. 29928.
## 22 OTHER metavelvet21 11915. 10127
## 23 OTHER metavelvet63 10915. 7386
## 24 OTHER metavelvet99 14350. 16673
## 25 OTHER ray21 25581. 29851
## 26 OTHER ray63 26903. 29862.
## 27 OTHER ray99 26895. 29862.
## 28 OTHER spades 32666. 29868.
## 29 OTHER trinity 42546. 33988
## 30 OTHER velvet21 11846. 10127
## 31 OTHER velvet63 11272. 7785
## 32 OTHER velvet99 14431. 17285
## 33 RNA-Seq abyss21 6962. 2647
## 34 RNA-Seq abyss63 16939. 19314.
## 35 RNA-Seq abyss99 21120. 27400
## 36 RNA-Seq megahit 24467. 29836
## 37 RNA-Seq metaspades 24726. 29840.
## 38 RNA-Seq metavelvet21 3598 1270.
## 39 RNA-Seq metavelvet63 14660. 16758.
## 40 RNA-Seq metavelvet99 18100. 19903
## 41 RNA-Seq ray21 35962. 30580
## 42 RNA-Seq ray63 31561. 30476.
## 43 RNA-Seq ray99 31561. 30476.
## 44 RNA-Seq spades 491775. 29904.
## 45 RNA-Seq trinity 47853. 40355
## 46 RNA-Seq velvet21 3713. 1192
## 47 RNA-Seq velvet63 14679. 16758.
## 48 RNA-Seq velvet99 18145. 19903
## 49 Targeted-Capture abyss21 25497. 28497
## 50 Targeted-Capture abyss63 22195. 29428.
## 51 Targeted-Capture abyss99 21532. 29289
## 52 Targeted-Capture megahit 27742. 29855
## 53 Targeted-Capture metaspades 32992. 29859
## 54 Targeted-Capture metavelvet21 6642. 4230
## 55 Targeted-Capture metavelvet63 9588. 7561
## 56 Targeted-Capture metavelvet99 17572. 22024
## 57 Targeted-Capture ray21 26393. 29856
## 58 Targeted-Capture ray63 26159. 29854
## 59 Targeted-Capture ray99 26142. 29854
## 60 Targeted-Capture spades 180248. 29873
## 61 Targeted-Capture trinity 36206. 32353
## 62 Targeted-Capture velvet21 6590. 4229
## 63 Targeted-Capture velvet63 9619. 8266
## 64 Targeted-Capture velvet99 18047. 22434
## 65 WGA abyss21 26214. 29506.
## 66 WGA abyss63 26751. 29680.
## 67 WGA abyss99 22042. 25441
## 68 WGA megahit 28036. 29821
## 69 WGA metaspades 28267. 29819
## 70 WGA metavelvet21 2131. 656.
## 71 WGA metavelvet63 4413. 2906.
## 72 WGA metavelvet99 6479. 6386.
## 73 WGA ray21 31125. 29842.
## 74 WGA ray63 30057. 29829
## 75 WGA ray99 30057. 29829
## 76 WGA spades 28812. 29874
## 77 WGA trinity 33503. 29850.
## 78 WGA velvet21 2195. 876.
## 79 WGA velvet63 4430. 3019
## 80 WGA velvet99 6403. 6214
#make_boxplot("unaligned contigs")
make_boxplot("N50")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 11643. 8250.
## 2 AMPLICON abyss63 8927. 4117
## 3 AMPLICON abyss99 4241. 1278.
## 4 AMPLICON megahit 22997. 28204
## 5 AMPLICON metaspades 23082. 29828
## 6 AMPLICON metavelvet21 671. 596
## 7 AMPLICON metavelvet63 890. 648
## 8 AMPLICON metavelvet99 708. 658
## 9 AMPLICON ray21 13430. 12080.
## 10 AMPLICON ray63 12477. 8068
## 11 AMPLICON ray99 12477. 8068
## 12 AMPLICON spades 16764. 15790.
## 13 AMPLICON trinity 15029. 13837
## 14 AMPLICON velvet21 673. 596
## 15 AMPLICON velvet63 880. 629
## 16 AMPLICON velvet99 706. 663
## 17 OTHER abyss21 12138. 6523
## 18 OTHER abyss63 15189. 16418
## 19 OTHER abyss99 16780. 18796
## 20 OTHER megahit 18282. 28185
## 21 OTHER metaspades 16174. 21584
## 22 OTHER metavelvet21 763 662
## 23 OTHER metavelvet63 805. 701
## 24 OTHER metavelvet99 1075. 792
## 25 OTHER ray21 15143. 9286.
## 26 OTHER ray63 16331. 17003
## 27 OTHER ray99 16336. 17003
## 28 OTHER spades 17970. 26503
## 29 OTHER trinity 15079. 12610.
## 30 OTHER velvet21 781. 665
## 31 OTHER velvet63 839. 710.
## 32 OTHER velvet99 1070. 792
## 33 RNA-Seq abyss21 885. 696
## 34 RNA-Seq abyss63 1233. 758
## 35 RNA-Seq abyss99 3561. 700
## 36 RNA-Seq megahit 2992. 919
## 37 RNA-Seq metaspades 2902. 813
## 38 RNA-Seq metavelvet21 589. 583
## 39 RNA-Seq metavelvet63 783. 734
## 40 RNA-Seq metavelvet99 1312. 1014.
## 41 RNA-Seq ray21 6800. 1273
## 42 RNA-Seq ray63 6099. 1234
## 43 RNA-Seq ray99 6097. 1228
## 44 RNA-Seq spades 7600. 2000
## 45 RNA-Seq trinity 2213. 895
## 46 RNA-Seq velvet21 585. 584
## 47 RNA-Seq velvet63 781. 729
## 48 RNA-Seq velvet99 1317. 1034
## 49 Targeted-Capture abyss21 13101. 8657
## 50 Targeted-Capture abyss63 13778. 3266
## 51 Targeted-Capture abyss99 12933. 2678
## 52 Targeted-Capture megahit 16162. 16225
## 53 Targeted-Capture metaspades 16179. 25414
## 54 Targeted-Capture metavelvet21 652. 594.
## 55 Targeted-Capture metavelvet63 865. 708.
## 56 Targeted-Capture metavelvet99 2458. 1038
## 57 Targeted-Capture ray21 14766. 5998
## 58 Targeted-Capture ray63 14280. 4193
## 59 Targeted-Capture ray99 14288. 4193
## 60 Targeted-Capture spades 18501. 28971
## 61 Targeted-Capture trinity 10320. 6489
## 62 Targeted-Capture velvet21 672. 629
## 63 Targeted-Capture velvet63 869. 706.
## 64 Targeted-Capture velvet99 2377. 1053
## 65 WGA abyss21 15478. 22264.
## 66 WGA abyss63 8825. 5290.
## 67 WGA abyss99 2141. 1230.
## 68 WGA megahit 21716. 29839
## 69 WGA metaspades 21808. 29840
## 70 WGA metavelvet21 626. 629
## 71 WGA metavelvet63 653. 629
## 72 WGA metavelvet99 647. 632.
## 73 WGA ray21 21991. 27294.
## 74 WGA ray63 21569. 27993
## 75 WGA ray99 21569. 27993
## 76 WGA spades 12524. 9287
## 77 WGA trinity 21034. 29817
## 78 WGA velvet21 625. 624
## 79 WGA velvet63 655. 634
## 80 WGA velvet99 648. 628
make_boxplot("N75")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 8296. 5720.
## 2 AMPLICON abyss63 6135. 2545
## 3 AMPLICON abyss99 2238. 713
## 4 AMPLICON megahit 19433. 26626
## 5 AMPLICON metaspades 19953. 29431
## 6 AMPLICON metavelvet21 593. 556
## 7 AMPLICON metavelvet63 704. 560
## 8 AMPLICON metavelvet99 608. 584
## 9 AMPLICON ray21 9378. 6886.
## 10 AMPLICON ray63 9410. 6212.
## 11 AMPLICON ray99 9410. 6212.
## 12 AMPLICON spades 11730. 7612.
## 13 AMPLICON trinity 11173. 7110.
## 14 AMPLICON velvet21 593. 558
## 15 AMPLICON velvet63 721. 560
## 16 AMPLICON velvet99 610. 586
## 17 OTHER abyss21 10849. 2901
## 18 OTHER abyss63 12882. 4747
## 19 OTHER abyss99 15833. 12065
## 20 OTHER megahit 13674. 3486.
## 21 OTHER metaspades 7465. 1836
## 22 OTHER metavelvet21 622. 571
## 23 OTHER metavelvet63 635. 587
## 24 OTHER metavelvet99 730. 608
## 25 OTHER ray21 10226. 2140.
## 26 OTHER ray63 11503. 2839
## 27 OTHER ray99 11504. 2839
## 28 OTHER spades 12898. 4170.
## 29 OTHER trinity 5104. 1646
## 30 OTHER velvet21 618. 572
## 31 OTHER velvet63 658. 594
## 32 OTHER velvet99 749. 633
## 33 RNA-Seq abyss21 745. 569
## 34 RNA-Seq abyss63 1025. 606.
## 35 RNA-Seq abyss99 2637. 633
## 36 RNA-Seq megahit 1228. 648
## 37 RNA-Seq metaspades 1604. 617
## 38 RNA-Seq metavelvet21 543. 534
## 39 RNA-Seq metavelvet63 606. 590
## 40 RNA-Seq metavelvet99 953. 788.
## 41 RNA-Seq ray21 2310. 776
## 42 RNA-Seq ray63 1766. 781
## 43 RNA-Seq ray99 1770. 781
## 44 RNA-Seq spades 3867. 902
## 45 RNA-Seq trinity 935. 620
## 46 RNA-Seq velvet21 542. 536.
## 47 RNA-Seq velvet63 606. 589
## 48 RNA-Seq velvet99 954. 806
## 49 Targeted-Capture abyss21 12054. 5664.
## 50 Targeted-Capture abyss63 13548. 1926
## 51 Targeted-Capture abyss99 12228. 1072
## 52 Targeted-Capture megahit 15248. 9211
## 53 Targeted-Capture metaspades 14706. 14101
## 54 Targeted-Capture metavelvet21 582. 551
## 55 Targeted-Capture metavelvet63 679. 578.
## 56 Targeted-Capture metavelvet99 1733. 754
## 57 Targeted-Capture ray21 13918. 2950
## 58 Targeted-Capture ray63 13516. 2070
## 59 Targeted-Capture ray99 13521. 2220
## 60 Targeted-Capture spades 16637. 21340
## 61 Targeted-Capture trinity 7605. 2533
## 62 Targeted-Capture velvet21 595. 554
## 63 Targeted-Capture velvet63 676. 576
## 64 Targeted-Capture velvet99 1726. 750
## 65 WGA abyss21 13161. 11025
## 66 WGA abyss63 5769. 3188.
## 67 WGA abyss99 1490. 788.
## 68 WGA megahit 17146. 29822
## 69 WGA metaspades 18249. 29836
## 70 WGA metavelvet21 565. 562.
## 71 WGA metavelvet63 580. 550
## 72 WGA metavelvet99 562. 554
## 73 WGA ray21 18047. 12058
## 74 WGA ray63 18708. 24722.
## 75 WGA ray99 18708. 24722.
## 76 WGA spades 8464. 4127
## 77 WGA trinity 14603. 7420.
## 78 WGA velvet21 567. 562.
## 79 WGA velvet63 575. 555
## 80 WGA velvet99 564. 553
#make_boxplot("L50")
make_boxplot("L75")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 4.4 3
## 2 AMPLICON abyss63 5.79 5
## 3 AMPLICON abyss99 8.65 7.5
## 4 AMPLICON megahit 3.27 1
## 5 AMPLICON metaspades 2.60 1
## 6 AMPLICON metavelvet21 3.44 2
## 7 AMPLICON metavelvet63 5.59 4
## 8 AMPLICON metavelvet99 8.10 7
## 9 AMPLICON ray21 4.13 2
## 10 AMPLICON ray63 4.55 3
## 11 AMPLICON ray99 4.56 3
## 12 AMPLICON spades 4.20 3
## 13 AMPLICON trinity 4.39 2
## 14 AMPLICON velvet21 3.44 2
## 15 AMPLICON velvet63 5.78 4
## 16 AMPLICON velvet99 8.21 7
## 17 OTHER abyss21 19.0 3
## 18 OTHER abyss63 17.0 3
## 19 OTHER abyss99 13.7 1.5
## 20 OTHER megahit 36.5 2
## 21 OTHER metaspades 31.3 3
## 22 OTHER metavelvet21 28.4 7
## 23 OTHER metavelvet63 24 8
## 24 OTHER metavelvet99 23.4 11
## 25 OTHER ray21 12.1 2.5
## 26 OTHER ray63 11.3 3
## 27 OTHER ray99 11.3 3
## 28 OTHER spades 25 2
## 29 OTHER trinity 41.3 8
## 30 OTHER velvet21 28.4 6
## 31 OTHER velvet63 25.2 9.5
## 32 OTHER velvet99 23.3 11
## 33 RNA-Seq abyss21 277. 42
## 34 RNA-Seq abyss63 116. 16.5
## 35 RNA-Seq abyss99 6.46 3
## 36 RNA-Seq megahit 365. 94
## 37 RNA-Seq metaspades 447. 128
## 38 RNA-Seq metavelvet21 124. 23
## 39 RNA-Seq metavelvet63 116. 18
## 40 RNA-Seq metavelvet99 6.95 3
## 41 RNA-Seq ray21 178. 24
## 42 RNA-Seq ray63 146. 19
## 43 RNA-Seq ray99 146. 19
## 44 RNA-Seq spades 397. 111
## 45 RNA-Seq trinity 519. 111
## 46 RNA-Seq velvet21 125. 30.5
## 47 RNA-Seq velvet63 116. 19
## 48 RNA-Seq velvet99 6.97 3
## 49 Targeted-Capture abyss21 5.71 2
## 50 Targeted-Capture abyss63 6.65 3
## 51 Targeted-Capture abyss99 7.18 3
## 52 Targeted-Capture megahit 69.0 2
## 53 Targeted-Capture metaspades 65.1 2
## 54 Targeted-Capture metavelvet21 51.1 5
## 55 Targeted-Capture metavelvet63 31.6 7
## 56 Targeted-Capture metavelvet99 22.3 7
## 57 Targeted-Capture ray21 23.4 4
## 58 Targeted-Capture ray63 21.2 5
## 59 Targeted-Capture ray99 21.1 5
## 60 Targeted-Capture spades 86.4 2
## 61 Targeted-Capture trinity 49.2 5
## 62 Targeted-Capture velvet21 51.3 5
## 63 Targeted-Capture velvet63 31.6 7
## 64 Targeted-Capture velvet99 22.3 7
## 65 WGA abyss21 8.39 2
## 66 WGA abyss63 13.4 4
## 67 WGA abyss99 18.4 13
## 68 WGA megahit 23.3 1
## 69 WGA metaspades 18.6 1
## 70 WGA metavelvet21 12.6 3.5
## 71 WGA metavelvet63 16.9 8
## 72 WGA metavelvet99 17.4 11
## 73 WGA ray21 9.12 2
## 74 WGA ray63 10.0 1
## 75 WGA ray99 10.0 1
## 76 WGA spades 18.4 3
## 77 WGA trinity 24.4 2
## 78 WGA velvet21 12.8 4
## 79 WGA velvet63 16.7 7
## 80 WGA velvet99 17.4 10
make_boxplot("NA50")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 12311. 8610
## 2 AMPLICON abyss63 9431. 4624.
## 3 AMPLICON abyss99 4517. 1480
## 4 AMPLICON megahit 24052. 29576
## 5 AMPLICON metaspades 23844. 29815
## 6 AMPLICON metavelvet21 692. 657
## 7 AMPLICON metavelvet63 945. 629
## 8 AMPLICON metavelvet99 685. 641
## 9 AMPLICON ray21 13095. 11269
## 10 AMPLICON ray63 12850. 8323
## 11 AMPLICON ray99 12849. 8323
## 12 AMPLICON spades 16582. 15378
## 13 AMPLICON trinity 14977. 16192
## 14 AMPLICON velvet21 692. 657
## 15 AMPLICON velvet63 933. 628
## 16 AMPLICON velvet99 682. 644
## 17 OTHER abyss21 13954. 12461
## 18 OTHER abyss63 18055. 24782
## 19 OTHER abyss99 20017. 27631
## 20 OTHER megahit 23159. 29831
## 21 OTHER metaspades 20154. 24810
## 22 OTHER metavelvet21 991. 732
## 23 OTHER metavelvet63 928. 783
## 24 OTHER metavelvet99 1250. 1128.
## 25 OTHER ray21 16659. 24313
## 26 OTHER ray63 18842. 27680
## 27 OTHER ray99 18846. 27680
## 28 OTHER spades 21927. 29822
## 29 OTHER trinity 18770. 24148
## 30 OTHER velvet21 1045. 734
## 31 OTHER velvet63 937. 794.
## 32 OTHER velvet99 1238. 1093
## 33 RNA-Seq abyss21 2257. 562
## 34 RNA-Seq abyss63 4235. 1354
## 35 RNA-Seq abyss99 7991. 3324
## 36 RNA-Seq megahit 27302. 29826
## 37 RNA-Seq metaspades 24294 29694.
## 38 RNA-Seq metavelvet21 522 522
## 39 RNA-Seq metavelvet63 1111. 1058.
## 40 RNA-Seq metavelvet99 1716. 823
## 41 RNA-Seq ray21 20410 22429
## 42 RNA-Seq ray63 19170. 22405
## 43 RNA-Seq ray99 19170. 22405
## 44 RNA-Seq spades 22049. 21438
## 45 RNA-Seq trinity 7027 3868
## 46 RNA-Seq velvet21 563 563
## 47 RNA-Seq velvet63 1049 1058.
## 48 RNA-Seq velvet99 1716. 823
## 49 Targeted-Capture abyss21 17890. 25017
## 50 Targeted-Capture abyss63 18046. 27292
## 51 Targeted-Capture abyss99 15943. 20376
## 52 Targeted-Capture megahit 22721. 29853
## 53 Targeted-Capture metaspades 22049. 26501
## 54 Targeted-Capture metavelvet21 680. 649
## 55 Targeted-Capture metavelvet63 945. 768
## 56 Targeted-Capture metavelvet99 2820. 1297
## 57 Targeted-Capture ray21 20049. 28592
## 58 Targeted-Capture ray63 18926. 28367
## 59 Targeted-Capture ray99 18937. 28367
## 60 Targeted-Capture spades 23079. 29852
## 61 Targeted-Capture trinity 13276. 9350.
## 62 Targeted-Capture velvet21 698. 670.
## 63 Targeted-Capture velvet63 949. 768
## 64 Targeted-Capture velvet99 2755. 1302
## 65 WGA abyss21 18930. 22283
## 66 WGA abyss63 10582. 6212.
## 67 WGA abyss99 2435. 1306.
## 68 WGA megahit 28827. 29821
## 69 WGA metaspades 28976. 29820.
## 70 WGA metavelvet21 659 664
## 71 WGA metavelvet63 596. 571
## 72 WGA metavelvet99 608. 590.
## 73 WGA ray21 23264. 26559
## 74 WGA ray63 23400. 28610
## 75 WGA ray99 23400. 28610
## 76 WGA spades 16004. 15306.
## 77 WGA trinity 27302. 29822
## 78 WGA velvet21 666. 666.
## 79 WGA velvet63 601. 571
## 80 WGA velvet99 597. 584.
make_boxplot("NA75")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 8901. 6368
## 2 AMPLICON abyss63 6416. 3052.
## 3 AMPLICON abyss99 2379. 866.
## 4 AMPLICON megahit 21131. 29576
## 5 AMPLICON metaspades 21652. 29819
## 6 AMPLICON metavelvet21 618. 579
## 7 AMPLICON metavelvet63 735. 560
## 8 AMPLICON metavelvet99 576. 560.
## 9 AMPLICON ray21 9177. 6428
## 10 AMPLICON ray63 9688. 6361
## 11 AMPLICON ray99 9688. 6361
## 12 AMPLICON spades 12399. 7651
## 13 AMPLICON trinity 11455. 7424
## 14 AMPLICON velvet21 618. 579
## 15 AMPLICON velvet63 753. 564
## 16 AMPLICON velvet99 581. 570.
## 17 OTHER abyss21 12678. 6860
## 18 OTHER abyss63 15859. 25333
## 19 OTHER abyss99 19303. 27634.
## 20 OTHER megahit 20221. 29830.
## 21 OTHER metaspades 11205. 4740
## 22 OTHER metavelvet21 675. 620.
## 23 OTHER metavelvet63 678. 604
## 24 OTHER metavelvet99 826. 784
## 25 OTHER ray21 12606. 3925
## 26 OTHER ray63 14420. 4818.
## 27 OTHER ray99 14421. 4818.
## 28 OTHER spades 18643. 27692.
## 29 OTHER trinity 8432. 3705
## 30 OTHER velvet21 685. 620.
## 31 OTHER velvet63 711. 655
## 32 OTHER velvet99 854. 791
## 33 RNA-Seq abyss21 3403. 609
## 34 RNA-Seq abyss63 6532. 669
## 35 RNA-Seq abyss99 5761. 1432
## 36 RNA-Seq megahit 17790. 17790.
## 37 RNA-Seq metaspades 22845 29583
## 38 RNA-Seq metavelvet21 NaN NA
## 39 RNA-Seq metavelvet63 648. 643
## 40 RNA-Seq metavelvet99 993. 687
## 41 RNA-Seq ray21 18728 19310
## 42 RNA-Seq ray63 14595. 8413
## 43 RNA-Seq ray99 14595. 8413
## 44 RNA-Seq spades 12962. 12097
## 45 RNA-Seq trinity 6586 4816
## 46 RNA-Seq velvet21 NaN NA
## 47 RNA-Seq velvet63 648. 643
## 48 RNA-Seq velvet99 968. 687
## 49 Targeted-Capture abyss21 16969. 25390
## 50 Targeted-Capture abyss63 19125. 29044
## 51 Targeted-Capture abyss99 15528. 15621
## 52 Targeted-Capture megahit 21833. 29853
## 53 Targeted-Capture metaspades 20403. 26386
## 54 Targeted-Capture metavelvet21 595. 558
## 55 Targeted-Capture metavelvet63 714. 593
## 56 Targeted-Capture metavelvet99 2009. 910.
## 57 Targeted-Capture ray21 18821. 28552.
## 58 Targeted-Capture ray63 18997. 28375
## 59 Targeted-Capture ray99 19004. 28375
## 60 Targeted-Capture spades 21944. 29852
## 61 Targeted-Capture trinity 10293. 5447
## 62 Targeted-Capture velvet21 601. 561
## 63 Targeted-Capture velvet63 709. 591
## 64 Targeted-Capture velvet99 2021. 960
## 65 WGA abyss21 16104. 22281
## 66 WGA abyss63 6894. 3974.
## 67 WGA abyss99 1700. 968
## 68 WGA megahit 28869 29821
## 69 WGA metaspades 28621 29821
## 70 WGA metavelvet21 647 647
## 71 WGA metavelvet63 547. 520.
## 72 WGA metavelvet99 534. 536
## 73 WGA ray21 20305. 26248.
## 74 WGA ray63 22866. 29146
## 75 WGA ray99 22866. 29146
## 76 WGA spades 12177. 7571
## 77 WGA trinity 23711. 29822.
## 78 WGA velvet21 647 647
## 79 WGA velvet63 549. 530.
## 80 WGA velvet99 527. 528.
#make_boxplot("LA50")
make_boxplot("LA75")
## # A tibble: 80 x 4
## # Groups: Assay_Type [5]
## Assay_Type X2 mean median
## <chr> <fct> <dbl> <dbl>
## 1 AMPLICON abyss21 3.93 2
## 2 AMPLICON abyss63 5.37 4.5
## 3 AMPLICON abyss99 8.58 8.5
## 4 AMPLICON megahit 1.59 1
## 5 AMPLICON metaspades 1.59 1
## 6 AMPLICON metavelvet21 2.47 2
## 7 AMPLICON metavelvet63 5.10 4
## 8 AMPLICON metavelvet99 7.48 7
## 9 AMPLICON ray21 3.90 3
## 10 AMPLICON ray63 4.04 3
## 11 AMPLICON ray99 4.05 3
## 12 AMPLICON spades 3.30 2
## 13 AMPLICON trinity 3.04 2
## 14 AMPLICON velvet21 2.47 2
## 15 AMPLICON velvet63 5.28 4
## 16 AMPLICON velvet99 7.61 7
## 17 OTHER abyss21 3.87 2
## 18 OTHER abyss63 2.51 1
## 19 OTHER abyss99 2.37 1
## 20 OTHER megahit 2.68 1
## 21 OTHER metaspades 3.07 2
## 22 OTHER metavelvet21 8.93 9.5
## 23 OTHER metavelvet63 8.35 9
## 24 OTHER metavelvet99 10.2 11
## 25 OTHER ray21 4.41 2
## 26 OTHER ray63 3.18 2
## 27 OTHER ray99 3.18 2
## 28 OTHER spades 2.77 1
## 29 OTHER trinity 6.77 3
## 30 OTHER velvet21 8.93 9.5
## 31 OTHER velvet63 8.64 10
## 32 OTHER velvet99 10.1 10.5
## 33 RNA-Seq abyss21 9.5 8
## 34 RNA-Seq abyss63 8.4 8
## 35 RNA-Seq abyss99 7 6
## 36 RNA-Seq megahit 1.5 1.5
## 37 RNA-Seq metaspades 1.33 1
## 38 RNA-Seq metavelvet21 NaN NA
## 39 RNA-Seq metavelvet63 10.3 14
## 40 RNA-Seq metavelvet99 11.5 10
## 41 RNA-Seq ray21 2.25 2
## 42 RNA-Seq ray63 2.33 2
## 43 RNA-Seq ray99 2.33 2
## 44 RNA-Seq spades 76.9 54
## 45 RNA-Seq trinity 18.3 4
## 46 RNA-Seq velvet21 NaN NA
## 47 RNA-Seq velvet63 10.3 14
## 48 RNA-Seq velvet99 11.7 11
## 49 Targeted-Capture abyss21 2.59 1
## 50 Targeted-Capture abyss63 3.08 1
## 51 Targeted-Capture abyss99 3.71 1.5
## 52 Targeted-Capture megahit 2.20 1
## 53 Targeted-Capture metaspades 2.33 1
## 54 Targeted-Capture metavelvet21 6.27 5
## 55 Targeted-Capture metavelvet63 6.98 8
## 56 Targeted-Capture metavelvet99 7.24 7
## 57 Targeted-Capture ray21 3.58 1
## 58 Targeted-Capture ray63 3.98 1
## 59 Targeted-Capture ray99 3.98 1
## 60 Targeted-Capture spades 8.49 1
## 61 Targeted-Capture trinity 5.43 2
## 62 Targeted-Capture velvet21 6.23 5
## 63 Targeted-Capture velvet63 7.06 7
## 64 Targeted-Capture velvet99 7.40 7
## 65 WGA abyss21 1.98 1
## 66 WGA abyss63 4.20 3
## 67 WGA abyss99 11.9 12
## 68 WGA megahit 1.05 1
## 69 WGA metaspades 1.06 1
## 70 WGA metavelvet21 1 1
## 71 WGA metavelvet63 4.42 3
## 72 WGA metavelvet99 8.54 9
## 73 WGA ray21 1.62 1
## 74 WGA ray63 1.37 1
## 75 WGA ray99 1.37 1
## 76 WGA spades 2.37 2
## 77 WGA trinity 1.43 1
## 78 WGA velvet21 1 1
## 79 WGA velvet63 4.04 3
## 80 WGA velvet99 8.65 8.5
#n50
make_boxplot_v2 = function(variableToPlot)
{
x4 = xm %>% filter(Assembly == variableToPlot)
stat = x4 %>%
group_by(X2, Assay_Type) %>%
summarize(mean = mean(value, na.rm = TRUE),
median = median(value, na.rm = TRUE))
print(stat)
plot = ggplot(x4, aes(X2, value)) +
geom_jitter(alpha = .5, width = .1, aes(color = X2)) +
geom_boxplot(alpha = 0.1) +
#scale_color_viridis(discrete=TRUE) +
#scale_fill_material("red") +
xlab("") +
ylab(variableToPlot) +
facet_grid(Assay_Type~., scales = "free") +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 90, hjust = 1),
strip.background =element_rect(fill="white"),
legend.position = "none")
print(plot)
filename = str_replace_all(variableToPlot, "[[:punct:]]", "")
ggsave(filename=paste(filename,".pdf", sep="_"), width = 12, height = 18, units = "cm", device = 'pdf')
}
#la50
make_boxplot_v2("L50")
## # A tibble: 80 x 4
## # Groups: X2 [16]
## X2 Assay_Type mean median
## <fct> <chr> <dbl> <dbl>
## 1 abyss21 AMPLICON 2.56 2
## 2 abyss21 OTHER 10.6 2
## 3 abyss21 RNA-Seq 155. 24
## 4 abyss21 Targeted-Capture 3.33 1
## 5 abyss21 WGA 5.08 1
## 6 abyss63 AMPLICON 3.22 3
## 7 abyss63 OTHER 9.18 1
## 8 abyss63 RNA-Seq 62.4 9
## 9 abyss63 Targeted-Capture 3.80 2
## 10 abyss63 WGA 7.51 2
## # … with 70 more rows
make_boxplot_v2("LA50")
## # A tibble: 80 x 4
## # Groups: X2 [16]
## X2 Assay_Type mean median
## <fct> <chr> <dbl> <dbl>
## 1 abyss21 AMPLICON 2.28 2
## 2 abyss21 OTHER 2.54 1.5
## 3 abyss21 RNA-Seq 6.43 8
## 4 abyss21 Targeted-Capture 1.71 1
## 5 abyss21 WGA 1.38 1
## 6 abyss63 AMPLICON 3.14 3
## 7 abyss63 OTHER 1.84 1
## 8 abyss63 RNA-Seq 7.3 5.5
## 9 abyss63 Targeted-Capture 2.46 1
## 10 abyss63 WGA 2.38 2
## # … with 70 more rows
make_boxplot_v2("Duplication ratio")
## # A tibble: 80 x 4
## # Groups: X2 [16]
## X2 Assay_Type mean median
## <fct> <chr> <dbl> <dbl>
## 1 abyss21 AMPLICON 1.02 1.00
## 2 abyss21 OTHER 1.00 1.00
## 3 abyss21 RNA-Seq 1.02 1
## 4 abyss21 Targeted-Capture 1.02 1.00
## 5 abyss21 WGA 1.01 1.00
## 6 abyss63 AMPLICON 1.02 1.01
## 7 abyss63 OTHER 1.00 1.00
## 8 abyss63 RNA-Seq 1.01 1.01
## 9 abyss63 Targeted-Capture 1.01 1.00
## 10 abyss63 WGA 1.01 1.01
## # … with 70 more rows
make_boxplot_v2("misassemblies")
## # A tibble: 80 x 4
## # Groups: X2 [16]
## X2 Assay_Type mean median
## <fct> <chr> <dbl> <dbl>
## 1 abyss21 AMPLICON 0.0128 0
## 2 abyss21 OTHER 0.0169 0
## 3 abyss21 RNA-Seq 0.0312 0
## 4 abyss21 Targeted-Capture 0 0
## 5 abyss21 WGA 0 0
## 6 abyss63 AMPLICON 0.0270 0
## 7 abyss63 OTHER 0 0
## 8 abyss63 RNA-Seq 0 0
## 9 abyss63 Targeted-Capture 0.0128 0
## 10 abyss63 WGA 0 0
## # … with 70 more rows
# genomic feature
x4 = xm %>% filter(Assembly == "genomic features")
x4$value.1 = gsub("part", "", x4$value.1)
x5 = data.frame(str_split_fixed(x4$value.1,"\\+", 2), x4)
x6 = data.frame(assembly = x5$X2, match = as.numeric(as.character(x5$X1.1)), mismatch = as.numeric(as.character(x5$X2.1)))
# need to add assay_type
ggplot(x6, aes(assembly, 100*(match/49))) +
geom_boxplot(aes(color = assembly)) +
geom_jitter(alpha = .5, width = .1, aes(color = assembly)) +
xlab("") +
ylab("% features mapped") +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 90, hjust = 1))
ggsave("percent features mapped.pdf", width = 15, height = 10, units = "cm")
90% of the genome is covered by single contig
library(tidyverse)
setwd("~/Documents/research/asm/covid19-Assembly/plots/")
#90%=29903*.9
contig = xm %>% filter(Assembly == "Largest contig") %>% filter(value >=26912.7)
ggplot(contig, aes(X2)) +
geom_histogram(stat = "count") +
xlab("") +
ylab("Count") +
facet_grid(~Assay_Type) +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
strip.background =element_rect(fill="white"),
axis.text = element_text(color = "black", angle = 90, hjust = 1))
ggsave("90_percent_genome_single_contig.pdf", width = 20, height = 6, units = "cm")
correlation read vs genome
library(tidyverse)
setwd("~/Documents/research/asm/covid19-Assembly/plots/")
#r = read.table("~/Documents/research/asm/covid19-Assembly/files//read_QC_matrix.txt")[,c(1,4)]
r = read.table("~/Documents/research/asm/covid19-Assembly/files/read_QC_matrix.txt")[,c(1,4)]
r2 = data.frame(id = str_split_fixed(r$V1, "_", 2), read = r$V4)
head(r2$id[,1])
## NULL
#sum every two rows of PE data
r3 = data.frame(id = unique(r2$id.1), read = (rowsum(r2[,3], as.integer(gl(nrow(r2), 2, nrow(r2))))))
rx = xm %>% filter(Assembly == "Genome fraction (%)")
rx2 = inner_join(r3, rx, by = c("id" = "X1"))
#rxm = left_join(rx2, meta2, by = c("id" = "Run"))
subset(is.na(rx2)) %>% dim()
## [1] 6027 9
subset(!is.na(rx2)) %>% dim()
## [1] 6027 9
# metavelve and ray have lots of NA genome frac values
ggplot(rx2, aes(value, read/1e6)) +
geom_point(aes(color = X2), shape = 18) +
geom_smooth(method='lm', formula= y~x) +
xlab("Genome fraction (%)") +
ylab("Number of reads (million)") +
facet_grid(~Assay_Type, scales = "free") +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
strip.background =element_rect(fill="white"),
axis.text = element_text(color = "black", angle = 0, hjust = 1),
legend.position = "none")
ggsave("readVsGenome.pdf", width = 20, height = 6, units = "cm")
rx3 = na.omit(rx2)
cor(rx3$value, rx3$read, method = "spearman")
## [1] 0.09714612
#read dist
dist = rx2 %>% distinct(id, .keep_all = T)
ggplot(dist, aes(Assay_Type, log10(read))) +
geom_boxplot(notch = T, width = .5) +
geom_jitter(alpha = .5, width = .1) +
xlab("") +
ylab("log10(Number of reads)") +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
strip.background =element_rect(fill="white"),
axis.text = element_text(color = "black", angle = 90, hjust = 1))
ggsave("readDist.pdf", width = 7, height = 10, units = "cm")
sample to assembler
library(tidyverse)
setwd("~/Documents/research/asm/covid19-Assembly/plots/")
rx2 %>% filter(Assembly == "Genome fraction (%)") %>% na.omit() %>%
ggplot(aes(fct_reorder(id, read), X2, color = X2)) +
geom_point(aes(size = value, alpha = .5)) +
ylab("") +
xlab("Samples are sorted by number of reads") +
theme(panel.background = element_rect(fill = "white"),
panel.border = element_rect(fill = NA, colour = "black", size = .5),
axis.text = element_text(color = "black", angle = 0, hjust = 1),
axis.text.x=element_blank())
ggsave("sampleVsassembler_color_bk.pdf", width = 30, height = 20, units = "cm")
# select 4 samples with high and low mean frac to plot in mvista
#high = SRR11578289 (97.4), SRR11597206 (94.4)
#low = SRR11828432, SRR11828424
#ref: MN908947.3
mvista = x3 %>% filter(Assembly == "Genome fraction (%)") %>% na.omit() %>%
group_by(X1) %>%
summarise(mean = mean(value)) %>% arrange(mean)
dataset preparation
library(tidyverse)
library(knitr)
c = read_tsv("~/Documents/research/asm/covid19-Assembly/files/SraRunTable_COVID19_14.06.20.txt")
c2 = tibble(c$Platform, c$Run, c$SRA_Sample, c$Instrument, c$LibraryLayout, c$Assay_Type, c$LibrarySelection, c$LibrarySource, c$Organism, c$geo_loc_name, c$host, c$host_disease, c$Consent)
names(c2) <- gsub("c\\$", "", names(c2))
colSums(!is.na(c2))
## Platform Run SRA_Sample Instrument
## 15007 15007 15007 15007
## LibraryLayout Assay_Type LibrarySelection LibrarySource
## 15007 15007 15007 15007
## Organism geo_loc_name host host_disease
## 15007 5508 5373 5297
## Consent
## 15007
#
#write_csv(c2, "~/Documents/research/asm/covid19-Assembly/files//COVID19_14.06.20_metadata_final.csv")
# summarise metadata
#colnames(c2)
#good code example: https://uc-r.github.io/descriptives_categorical
table3 <- table(c2$Instrument, c2$Assay_Type)
table3 <- table( c2$Assay_Type, c2$LibrarySource)
table3 <- table(c2$Assay_Type, c2$LibrarySource, c2$LibraryLayout)
ftable(table3)
## PAIRED SINGLE
##
## AMPLICON GENOMIC 0 0
## METAGENOMIC 0 41
## METATRANSCRIPTOMIC 0 0
## SYNTHETIC 0 136
## TRANSCRIPTOMIC 0 0
## VIRAL RNA 5254 6507
## OTHER GENOMIC 0 0
## METAGENOMIC 1 0
## METATRANSCRIPTOMIC 0 0
## SYNTHETIC 0 0
## TRANSCRIPTOMIC 0 0
## VIRAL RNA 68 0
## RNA-Seq GENOMIC 1 0
## METAGENOMIC 9 9
## METATRANSCRIPTOMIC 9 0
## SYNTHETIC 0 0
## TRANSCRIPTOMIC 16 3
## VIRAL RNA 682 654
## Targeted-Capture GENOMIC 0 0
## METAGENOMIC 0 0
## METATRANSCRIPTOMIC 0 0
## SYNTHETIC 0 0
## TRANSCRIPTOMIC 0 0
## VIRAL RNA 194 241
## WGA GENOMIC 6 0
## METAGENOMIC 5 0
## METATRANSCRIPTOMIC 0 0
## SYNTHETIC 0 0
## TRANSCRIPTOMIC 0 0
## VIRAL RNA 1061 0
## WGS GENOMIC 11 0
## METAGENOMIC 6 0
## METATRANSCRIPTOMIC 0 0
## SYNTHETIC 0 0
## TRANSCRIPTOMIC 0 0
## VIRAL RNA 93 0
# will add table paper
df = c2 %>%
group_by(Assay_Type, LibrarySource, LibraryLayout) %>%
tally()
kable(df, caption = "Summary of all data. This table will add table paper")
Summary of all data. This table will add table paper
| AMPLICON |
METAGENOMIC |
SINGLE |
41 |
| AMPLICON |
SYNTHETIC |
SINGLE |
136 |
| AMPLICON |
VIRAL RNA |
PAIRED |
5254 |
| AMPLICON |
VIRAL RNA |
SINGLE |
6507 |
| OTHER |
METAGENOMIC |
PAIRED |
1 |
| OTHER |
VIRAL RNA |
PAIRED |
68 |
| RNA-Seq |
GENOMIC |
PAIRED |
1 |
| RNA-Seq |
METAGENOMIC |
PAIRED |
9 |
| RNA-Seq |
METAGENOMIC |
SINGLE |
9 |
| RNA-Seq |
METATRANSCRIPTOMIC |
PAIRED |
9 |
| RNA-Seq |
TRANSCRIPTOMIC |
PAIRED |
16 |
| RNA-Seq |
TRANSCRIPTOMIC |
SINGLE |
3 |
| RNA-Seq |
VIRAL RNA |
PAIRED |
682 |
| RNA-Seq |
VIRAL RNA |
SINGLE |
654 |
| Targeted-Capture |
VIRAL RNA |
PAIRED |
194 |
| Targeted-Capture |
VIRAL RNA |
SINGLE |
241 |
| WGA |
GENOMIC |
PAIRED |
6 |
| WGA |
METAGENOMIC |
PAIRED |
5 |
| WGA |
VIRAL RNA |
PAIRED |
1061 |
| WGS |
GENOMIC |
PAIRED |
11 |
| WGS |
METAGENOMIC |
PAIRED |
6 |
| WGS |
VIRAL RNA |
PAIRED |
93 |
#write_tsv(df, "~/Documents/research/asm/covid19-Assembly/files//summary_data.tsv")
df2 = df %>% filter(LibrarySource == "VIRAL RNA")
kable(df2, caption = "Summary of VIRAL RNA data")
Summary of VIRAL RNA data
| AMPLICON |
VIRAL RNA |
PAIRED |
5254 |
| AMPLICON |
VIRAL RNA |
SINGLE |
6507 |
| OTHER |
VIRAL RNA |
PAIRED |
68 |
| RNA-Seq |
VIRAL RNA |
PAIRED |
682 |
| RNA-Seq |
VIRAL RNA |
SINGLE |
654 |
| Targeted-Capture |
VIRAL RNA |
PAIRED |
194 |
| Targeted-Capture |
VIRAL RNA |
SINGLE |
241 |
| WGA |
VIRAL RNA |
PAIRED |
1061 |
| WGS |
VIRAL RNA |
PAIRED |
93 |
##subsample main paper
#PE
set.seed(2020)
a1 = c2 %>% filter(LibraryLayout == "PAIRED" & Assay_Type == "AMPLICON" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "PE: AMPLICON of VIRAL RNA") %>% sample_n(100)
set.seed(2020)
a2 = c2 %>% filter(LibraryLayout == "PAIRED" & Assay_Type == "OTHER" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "PE: OTHER of VIRAL RNA")
set.seed(2020)
a3 = c2 %>% filter(LibraryLayout == "PAIRED" & Assay_Type == "RNA-Seq" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "PE: RNA-Seq of VIRAL RNA") %>% sample_n(100)
set.seed(2020)
a4 = c2 %>% filter(LibraryLayout == "PAIRED" & Assay_Type == "Targeted-Capture" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "PE: Targeted-Capture of VIRAL RNA") %>% sample_n(100)
set.seed(2020)
a5 = c2 %>% filter(LibraryLayout == "PAIRED" & Assay_Type == "WGA" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "PE: WGA of VIRAL RNA") %>% sample_n(100)
set.seed(2020)
a6 = c2 %>% filter(LibraryLayout == "PAIRED" & Assay_Type == "WGS" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "PE: WGS of VIRAL RNA")
#SE
set.seed(2020)
b1 = c2 %>% filter(LibraryLayout == "SINGLE" & Assay_Type == "AMPLICON" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "SE: AMPLICON of VIRAL RNA") %>% sample_n(100)
set.seed(2020)
b2 = c2 %>% filter(LibraryLayout == "SINGLE" & Assay_Type == "RNA-Seq" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "SE: RNA-Seq of VIRAL RNA") %>% sample_n(100)
set.seed(2020)
b3 = c2 %>% filter(LibraryLayout == "SINGLE" & Assay_Type == "Targeted-Capture" & LibrarySource == "VIRAL RNA") %>%
mutate(LibType = "SE: Targeted-Capture of VIRAL RNA") %>% sample_n(100)
#dataset
ab_pe = rbind(a1, a2, a3, a4, a5, a6)
ab_se = rbind(b1, b2, b3)
#write_csv(ab_pe, "~/Documents/research/asm/covid19-Assembly/files//PE_561samples_final.csv")
#write_csv(ab_se, "~/Documents/research/asm/covid19-Assembly/files//SE_300samples_final.csv")
#write.table(ab_pe$Run, "~/Documents/research/asm/covid19-Assembly/files//PE_561samples_final_561runs.txt", col.names = F, row.names = F, quote = F)
#write.table(ab_se$Run, "~/Documents/research/asm/covid19-Assembly/files//SE_300samples_final_300runs.txt", col.names = F, row.names = F, quote = F)
#write_csv(a1, "~/Documents/research/asm/covid19-Assembly/files//PE_100samples_amplicon_bioRxiv.csv")
#write.table(a1[,2], "~/Documents/research/asm/covid19-Assembly/files//PE_100samples_amplicon_bioRxiv_100runs.txt", col.names = F, row.names = F, quote = F)
# test ram and cpu
set.seed(2020)
#20 samples will be selected from amplicon with similar depth
#ram = a1 %>% sample_n(20)
ab = rbind(ab_pe, ab_se)
ab2 = ab %>% group_by(LibType) %>% tally()
kable(ab2, caption = "List of total 9 different categories. Maximum 100 samples are randomly selected")
List of total 9 different categories. Maximum 100 samples are randomly selected
| PE: AMPLICON of VIRAL RNA |
100 |
| PE: OTHER of VIRAL RNA |
68 |
| PE: RNA-Seq of VIRAL RNA |
100 |
| PE: Targeted-Capture of VIRAL RNA |
100 |
| PE: WGA of VIRAL RNA |
100 |
| PE: WGS of VIRAL RNA |
93 |
| SE: AMPLICON of VIRAL RNA |
100 |
| SE: RNA-Seq of VIRAL RNA |
100 |
| SE: Targeted-Capture of VIRAL RNA |
100 |